The Power of (Statistical) Relational Thinking
Taking into account relational structure during data mining can lead to better results, both in terms of quality and computational efficiency. This structure may be captured in the schema, in links between entities (e.g., graphs) or in rules describing the domain (e.g., knowledge graphs). Further, for richly structured prediction problems, there is often a need for a mix of both logical reasoning and statistical inference. In this talk, I will give an introduction to the field of Statistical Relational Learning (SRL), and I’ll identify useful tips and tricks for exploiting structure in both the input and output space. I’ll describe our recent work on highly scalable approaches for statistical relational inference. I’ll close by introducing a broader interpretation of relational thinking that reveals new research opportunities (and challenges!).
Bio: Lise Getoor is a Professor in the Computer Science & Engineering Department at UC Santa Cruz, where she holds the Jack Baskin Endowed Chair in Computer Engineering. She is founding Director of the UC Santa Cruz Data Science Research Center and is a Fellow of ACM, AAAI, and IEEE. Her research areas include machine learning and reasoning under uncertainty. She has extensive experience with machine learning and probabilistic modeling methods for graph and network data. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a Professor at the University of Maryland, College Park from 2001-2013.
AI for social impact: Results from deployments for public health and conversation
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I will focus on domains of public health and conservation, and address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. I will present results from work around the globe in using AI for challenges in public health such as Maternal and Child care interventions, HIV prevention, and in conservation such as endangered wildlife protection. Achieving social impact in these domains often requires methodological advances. To that end, I will highlight key research advances in multiagent reasoning and learning, in particular in, restless multiarmed bandits, influence maximization in social networks, computational game theory and decision-focused learning. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques.
Bio: Milind Tambe is Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University; concurrently, he is also Director "AI for Social Good" at Google Research. He is recipient of the IJCAI (International Joint Conference on AI) John McCarthy Award, AAMAS ACM (Association for Computing Machinery) Autonomous Agents Research Award, AAAI (Association for Advancement of Artificial Intelligence) Robert S. Engelmore Memorial Lecture Award, and he is a fellow of AAAI and ACM. He is also a recipient of the INFORMS Wagner prize for excellence in Operations Research practice and Rist Prize from MORS (Military Operations Research Society). For his work on AI and public safety, he has received Columbus Fellowship Foundation Homeland security award and commendations and certificates of appreciation from the US Coast Guard, the Federal Air Marshals Service and airport police at the city of Los Angeles.
Beyond Traditional Characterizations in the Age of Data: Big Models, Scalable Algorithms, and Meaningful Solutions
What are data and network models? What are efficient algorithms? What are meaningful solutions? Big Data, Network Sciences, and Machine Learning have fundamentally challenged the basic characterizations in computing, from the conventional graph-theoretical modeling of networks to the traditional polynomial-time worst-case measures of efficiency:
-- For a long time, graphs have been widely used for defining the structure of social and information networks. However, real-world network data and phenomena are much richer and more complex than what can be captured by nodes and edges. Network data is multifaceted, and thus network sciences require new theories, going beyond classic graph theory and graph-theoretical frameworks, to capture the multifaceted data.
-- More than ever before, it is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or even sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation.
In this talk, I will discuss some aspects of these challenges. Using basic tasks in network analysis, geometric embedding, game theory, and machine learning as examples, I will highlight the role of big models, scalable algorithms and axiomatization in shaping our understanding of "effective solution concepts," which need to be both mathematically meaningful and algorithmically efficient.
Bio: Shang-Hua Teng is a University Professor and Seely G. Mudd Professor of Computer Science and Mathematics at USC. He is a fellow of SIAM, ACM, and Alfred P. Sloan Foundation, and has twice won the Gödel Prize, first in 2008, for developing smoothed analysis, and then in 2015, for designing the breakthrough scalable Laplacian solver. Citing him as, “one of the most original theoretical computer scientists in the world”, the Simons Foundation named him a 2014 Simons Investigator to pursue long-term curiosity-driven fundamental research. He also received the 2009 Fulkerson Prize, 2021 ACM STOC Test of Time Award (for smoothed analysis), 2022 ACM SIGecom Test of Time Award (for settling the complexity of computing a Nash equilibrium), and 2011 ACM STOC Best Paper Award (for improving maximum-flow minimum-cut algorithms). In addition, he and collaborators developed the first optimal well-shaped Delaunay mesh generation algorithms for arbitrary three-dimensional domains, settled the Rousseeuw-Hubert regression-depth conjecture in robust statistics, and resolved two long-standing complexity-theoretical questions regarding the Sprague-Grundy theorem in combinatorial game theory. For his industry work with Xerox, NASA, Intel, IBM, Akamai, and Microsoft, he received fifteen patents in areas including compiler optimization, Internet technology, and social networks. Dedicated to teaching his daughter to speak Chinese as the sole Chinese-speaking parent in an otherwise English-speaking family and environment, he has also become fascinated with children's bilingual learning.